In [1]:
import plotly.graph_objects as go
import plotly.figure_factory as ff
import plotly
import plotly.io as pio
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams.update({'figure.max_open_warning': 0})
%matplotlib inline

import pandas as pd
import numpy as np

pd.set_option('display.max_columns', None)
In [2]:
pio.templates
Out[2]:
Templates configuration
-----------------------
    Default template: 'plotly'
    Available templates:
        ['ggplot2', 'seaborn', 'simple_white', 'plotly',
         'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',
         'ygridoff', 'gridon', 'none']
In [3]:
filename = 'caso_full.csv'
#df = pd.read_csv(filename, header=0, index_col=0, parse_dates=True, squeeze=True, nrows=100000)
df = pd.read_csv(filename, header=0, index_col=0, parse_dates=True, squeeze=True)
df = df[df['place_type'] == 'state']
df
Out[3]:
city_ibge_code date epidemiological_week estimated_population estimated_population_2019 is_last is_repeated last_available_confirmed last_available_confirmed_per_100k_inhabitants last_available_date last_available_death_rate last_available_deaths order_for_place place_type state new_confirmed new_deaths
city
NaN 12.0 2020-03-17 202012 894470.0 881935.0 False False 3 0.33539 2020-03-17 0.0000 0 1 state AC 3 0
NaN 12.0 2020-03-18 202012 894470.0 881935.0 False False 3 0.33539 2020-03-18 0.0000 0 2 state AC 0 0
NaN 12.0 2020-03-19 202012 894470.0 881935.0 False False 4 0.44719 2020-03-19 0.0000 0 3 state AC 1 0
NaN 12.0 2020-03-20 202012 894470.0 881935.0 False False 7 0.78259 2020-03-20 0.0000 0 4 state AC 3 0
NaN 12.0 2020-03-21 202012 894470.0 881935.0 False False 11 1.22978 2020-03-21 0.0000 0 5 state AC 4 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
NaN 17.0 2021-08-07 202131 1590248.0 1572866.0 False False 212734 13377.41032 2021-08-07 0.0168 3564 508 state TO 159 4
NaN 17.0 2021-08-08 202132 1590248.0 1572866.0 False False 212851 13384.76766 2021-08-08 0.0168 3569 509 state TO 117 5
NaN 17.0 2021-08-09 202132 1590248.0 1572866.0 False False 213262 13410.61268 2021-08-09 0.0168 3576 510 state TO 411 7
NaN 17.0 2021-08-10 202132 1590248.0 1572866.0 True False 213478 13424.19547 2021-08-10 0.0168 3585 511 state TO 216 9
NaN 17.0 2021-08-11 202132 1590248.0 1572866.0 False True 213478 13424.19547 2021-08-10 0.0168 3585 512 state TO 0 0

13963 rows × 17 columns

In [4]:
df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 13963 entries, nan to nan
Data columns (total 17 columns):
 #   Column                                         Non-Null Count  Dtype  
---  ------                                         --------------  -----  
 0   city_ibge_code                                 13963 non-null  float64
 1   date                                           13963 non-null  object 
 2   epidemiological_week                           13963 non-null  int64  
 3   estimated_population                           13963 non-null  float64
 4   estimated_population_2019                      13963 non-null  float64
 5   is_last                                        13963 non-null  bool   
 6   is_repeated                                    13963 non-null  bool   
 7   last_available_confirmed                       13963 non-null  int64  
 8   last_available_confirmed_per_100k_inhabitants  13957 non-null  float64
 9   last_available_date                            13963 non-null  object 
 10  last_available_death_rate                      13963 non-null  float64
 11  last_available_deaths                          13963 non-null  int64  
 12  order_for_place                                13963 non-null  int64  
 13  place_type                                     13963 non-null  object 
 14  state                                          13963 non-null  object 
 15  new_confirmed                                  13963 non-null  int64  
 16  new_deaths                                     13963 non-null  int64  
dtypes: bool(2), float64(5), int64(6), object(4)
memory usage: 1.7+ MB
In [5]:
df.describe()
Out[5]:
city_ibge_code epidemiological_week estimated_population estimated_population_2019 last_available_confirmed last_available_confirmed_per_100k_inhabitants last_available_death_rate last_available_deaths order_for_place new_confirmed new_deaths
count 13963.000000 13963.000000 1.396300e+04 1.396300e+04 1.396300e+04 13957.000000 13963.000000 13963.000000 13963.000000 13963.000000 13963.000000
mean 29.172456 202068.212991 7.917636e+06 7.857543e+06 2.891813e+05 4541.170490 0.027477 7973.330874 259.107713 1450.648213 40.550956
std 12.759476 42.966500 9.242119e+06 9.176868e+06 4.518302e+05 4003.034959 0.017022 14491.827926 149.351443 2466.301158 84.406521
min 11.000000 202009.000000 6.311810e+05 6.057610e+05 0.000000e+00 0.002160 0.000000 0.000000 1.000000 -2845.000000 -84.000000
25% 17.000000 202030.000000 2.809394e+06 2.778986e+06 4.782950e+04 1226.610930 0.019400 943.000000 130.000000 239.000000 5.000000
50% 27.000000 202048.000000 4.064052e+06 4.018650e+06 1.506680e+05 3559.747640 0.023600 3539.000000 259.000000 701.000000 15.000000
75% 41.000000 202114.000000 9.616621e+06 9.557071e+06 3.367055e+05 7046.182350 0.030100 8766.500000 388.000000 1602.500000 39.000000
max 53.000000 202132.000000 4.628933e+07 4.591905e+07 4.138421e+06 19220.318740 0.222200 141664.000000 534.000000 64036.000000 1389.000000
In [6]:
estados = df['state'].unique()
estados
Out[6]:
array(['AC', 'AL', 'AM', 'AP', 'BA', 'CE', 'DF', 'ES', 'GO', 'MA', 'MG',
       'MS', 'MT', 'PA', 'PB', 'PE', 'PI', 'PR', 'RJ', 'RN', 'RO', 'RR',
       'RS', 'SC', 'SE', 'SP', 'TO'], dtype=object)
In [7]:
totalmortes = df.groupby('state')['new_deaths'].sum()

fig = go.Figure()
fig.add_trace(go.Bar(
        y=totalmortes,
        x=totalmortes.index,
        orientation='v',
        text=totalmortes,
        textposition='auto'
))

fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide', template='ggplot2', title_text='Total de Mortes por Estado')
fig.update_yaxes(visible=False, showticklabels=False)
fig.show()
In [8]:
estado = df['state'].unique()

fig = go.Figure()

for state in estado:
    fig.add_trace(go.Violin(x=df['state'][df['state'] == state],
                            y=df['new_deaths'][df['state'] == state],
                            name=state,
                            box_visible=True,
                            meanline_visible=True))


fig.update_layout(template='ggplot2', title_text='Análise de Mortes por Estado')

fig.show()
In [9]:
for uf in estados:
    df2 = df
    df2 = df2[df2['state'] == uf]
    series2 = df2.groupby(df2['date'])['new_deaths'].sum()
    rolling2 = series2.rolling(window=7)
    rolling_mean2 = rolling2.mean()
    plt.figure(figsize=(15,3))
    plt.title('Média Móvel: ' + uf)
    rolling_mean2.plot()
    #plt.show()
In [10]:
for uf in estados:
    df2 = df
    df2 = df2[df2['state'] == uf]
    series2 = df2.groupby(df2['date'])['new_deaths'].sum()
    series2 = series2.clip(lower=0)
    rolling2 = series2.rolling(window=7)
    rolling_mean2 = rolling2.mean()
    
    fig = go.Figure()
    
    fig.add_trace(go.Bar(
        y=series2,
        x=series2.index,
        name='Mortes',
        orientation='v',
        marker=dict(
            color='rgba(169,169,169, 0.6)',
            line=dict(color='rgba(169,169,169, 1.0)', width=3)
        )
    ))

    fig.add_trace(go.Line(
        y=rolling_mean2,
        x=rolling_mean2.index,
        name='Média Movel',
        marker=dict(
            color='rgba(165,42,42, 1)',
            line=dict(color='rgba(165,42,42, 1)', width=4)
        )
    ))

    fig.update_layout(barmode='stack')
    fig.update_layout(title_text=uf + ': Total de Mortes e Média Móvel', template='ggplot2')
    fig.update_layout(hovermode="x unified")
    fig.update_layout(hoverlabel=dict(bgcolor="white", font_size=16, font_family="Rockwell"))
    fig.show()

C:\Users\Liu\anaconda3\lib\site-packages\plotly\graph_objs\_deprecations.py:378: DeprecationWarning:

plotly.graph_objs.Line is deprecated.
Please replace it with one of the following more specific types
  - plotly.graph_objs.scatter.Line
  - plotly.graph_objs.layout.shape.Line
  - etc.